In the past decade, there has been a tremendous growth in streaming media applications, thanks to the fast development of network services and the remarkable growth of smart mobile devices. For instance, in the field of over-the-top (OTT) video delivery, several methods, such as HTTP Live Streaming (HLS), Silverlight Smooth Streaming (MSS), HTTP Dynamic Streaming (EDS), and Dynamic Adaptive Streaming over HTTP (DASH), achieve decoder-driven rate adaptation by providing video streams in a variety of bitrates and breaking them into small HTTP file segments. The media information of each segment is stored in a manifest file, which is created at server and transmitted to client to provide the specification and location of each segment. Throughout the streaming process, the video player at the client adaptively switches among the available streams by selecting segments based on playback rate, buffer condition and instantaneous TCP throughput. With the rapid growth of streaming media applications, there has been a strong demand of accurate Quality-of-Experience (QoE) measurement and QoE-driven adaptive video delivery methods.
Due to the increasing popularity of video streaming services, users are continuously raising their expectations on better services. There have been studies or surveys to investigate user preferences on the type of video delivery services, which tend to show a dominating role of QoE in the user choice over other categories such as content, timing, quality, ease-of-use, portability, interactivity, and sharing. Significant loss of revenue could be attributed to poor quality of video streams. It is believed that poor streaming experience may become a major threat to the video service ecosystem. Therefore, achieving optimal QoE of end viewers has been the central goal of modern video delivery services.
As the humans are the ultimate receiver of videos in most applications, subjective evaluation is often regarded as the most straightforward and reliable approach to evaluate the QoE of streaming videos. A comprehensive subjective user study has several benefits. It provides useful data to study human behaviors in evaluating perceived quality of streaming videos; it supplies a test set to evaluate, compare and optimize streaming strategies; and it is useful to validate and compare the performance of existing objective QoE models. Although such subjective user studies provide reliable evaluations, they are often inconvenient, time-consuming and expensive. More importantly, they are difficult to be applied in any real-time playback scheduling framework. Therefore, highly accurate, low complexity, objective measures are desirable to enable efficient design of quality-control and resource allocation protocols for media delivery systems. However, many known methods are designed to measure presentation quality (or picture quality) only or the impact of initial buffering and playback stalling only. In practice, existing systems often rely on bitrate and global statistics of stalling events for QoE prediction. This is problematic for at least two reasons. First, using the same bitrate to encode different video content can result in drastically different presentation quality. Second, the interactions between video presentation quality and network quality are difficult to account for or simply not accounted for in some of these known methods.
The forgoing creates challenges and constraints for making objective QoE measurement, in real time, and for large number of end users. There is therefore a need for a method and system for automating user quality-of-experience measurement of streaming video signals as compared to the existing art. It is an object of the present invention to mitigate or obviate at least one of the above mentioned disadvantages.